NDLI: Privacy preserving K-Medoids clustering: an approach towards securing data in Mobile cloud architecture

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Privacy preserving K-Medoids clustering: an approach towards securing data in Mobile cloud architecture

Content Provider	ACM Digital Library
Author	Mishra, Debi Pr. Mishra, Ranjita Dash, Sanjit Kumar Dash, Sweta
Abstract	The proliferation of mobile computing and cloud services is driving a revolutionary change in today's information society. We are moving into the Ubiquitous computing age in which a user utilizes, at the same time, several electronic platforms through which one can access all the required information whenever and wherever needed. Mobile users can use their cellular phone to check e-mail, browse internet; travelers with portable computers can surf the internet from airports, railway stations etc. The mobile capabilities can be integrated with cloud computing services to give more secure and advanced services to the subscribers. At the same time privacy is an important issue in the collaborative ubiquitous computing since privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. The main challenge arises as to how multiple parties collaboratively conduct information exchange without breaching data privacy. This paper seeks to investigate solutions for secure Mobile cloud architecture by using a privacy preserving K-Medoids clustering which is one of data mining tasks.
Starting Page	439
Ending Page	443
Page Count	5
File Format	PDF
ISBN	9781450313100
DOI	10.1145/2393216.2393290
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-26
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cloud computing Mobile cloud computing Clustering K-medoid
Content Type	Text
Resource Type	Article

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